Multi-kernel support vector machine classification method

Abstract

The invention discloses a classification method for a multi-kernel support vector machine, which relates to the artificial intelligence field, in particular to the data mining technology, and comprises a data pretreatment section, a kernel function selection section, a support vector machine realizing section, and a human-computer interaction section. The work processing comprises that users submit classification request of data to the DPP, then KSP chooses the kernel function, an SILP solution module converts a multi-kernel support vector machine problem to an SILP problem and then solves the problem, a condition detecting module detects whether the condition is satisfied, and if the condition is satisfied, the HIP returns the result to users, otherwise, the parameter and the objective function are updated, and the SILP solution module is transferred to solve. The invention improves the capability of processing complex data of the support vector machine through multi-kernel functions, promotes the complexity of a module and the calculation, and converts the multi-kernel support vector machine problem to a semi-infinite linear program for avoiding the increasing of kernel functions simultaneously, and solves through a method of global convergence.

Description

Technical field [0001] The invention relates to the field of artificial intelligence, in particular to data mining technology, and in particular to a multi-core support vector machine classification method. Background technique [0002] Support vector machine has been widely used as an effective data analysis method. When the data analysis task is relatively simple, the traditional support vector machine using a single kernel function can effectively classify data and solve problems on a data set with a single stable data source for given parameters. However, in the face of more complex heterogeneous data, the traditional support vector machine cannot effectively map all the characteristics of the model in the same feature space and the same parameters because it only uses one kernel function, so it cannot effectively train a decision function. , or a decision function with overgeneralization will be obtained, such a decision function will lead to inaccurate classification....

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Problems solved by technology

In addition, compared with the present invention, this method itself has higher space-time complexity and needs to consume a large amount of computing resources

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